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Remote Sensing in Marine-Coastal Environments

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 15 June 2024 | Viewed by 12454

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Dr. Sara Innangi

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Institute of Marine Sciences-National Research Council (ISMAR-CNR), Calata Porta di Massa, Interno Porto di Napoli, 80133 Naples, Italy
Interests: seafloor mapping; acquisition and processing of multibeam and sidescan sonar data; image analysis; habitat mapping; environmental science; sedimentology
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Dr. Michele Innangi

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Dep. Biosciences and Territory, University of Molise, Via Duca degli Abruzzi, I-86039 Termoli (CB), Italy
Interests: remote sensing; vegetation; plant biodiversity; soil ecology
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Special Issue Information

Dear Colleagues,

Marine-coastal environments host some of the most fragile and threatened ecosystems in the world. The effects of climate change, alterations to coastlines, anthropic pressure, and the invasion of alien species that affect the natural dynamics of these communities are among the greatest threats. In the last 10 years, research related to remote sensing has become increasingly prevalent for the study of these ecosystems at different spatial scales, both on land and at sea. As a matter of fact, this type of study is pivotal for the management of marine–coastal ecosystems, as well as a valuable tool in terms of forecasting future change scenarios.

In this Special Issue, we will focus on all types of remote sensing techniques, including those strictly related to water environments (e.g., multibeam bathymetry and backscatter data, side scan sonar survey, underwater surveys with photo/video capture or ROV) and those that can be applied on all land, coastal, and transitional environments (e.g., satellite or UAV imagery).

Any type of scientific contribution, research article, technical note, scientific review, or monitoring study on marine–coastal environments is welcome, with particular attention being paid to surveys aimed at the land–sea interface.

Dr. Sara Innangi
Dr. Michele Innangi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

26 pages, 9597 KiB

Open AccessArticle

Continuously Updated Digital Elevation Models (CUDEMs) to Support Coastal Inundation Modeling

by Christopher J. Amante, Matthew Love, Kelly Carignan, Michael G. Sutherland, Michael MacFerrin and Elliot Lim

Remote Sens. 2023, 15(6), 1702; https://doi.org/10.3390/rs15061702 - 22 Mar 2023

Cited by 8 | Viewed by 3592

Abstract

The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) generates digital elevation models (DEMs) that range from the local to global scale. Collectively, these DEMs are essential to determining the timing and extent of coastal inundation and improving community preparedness, event forecasting, and warning systems. We initiated a comprehensive framework at NCEI, the Continuously Updated DEM (CUDEM) Program, with seamless bare-earth, topographic-bathymetric and bathymetric DEMs for the entire United States (U.S.) Atlantic and Gulf of Mexico Coasts, Hawaii, American Territories, and portions of the U.S. Pacific Coast. The CUDEMs are currently the highest-resolution, seamless depiction of the entire U.S. Atlantic and Gulf Coasts in the public domain; coastal topographic-bathymetric DEMs have a spatial resolution of 1/9th arc-second (~3 m) and offshore bathymetric DEMs coarsen to 1/3rd arc-second (~10 m). We independently validate the land portions of the CUDEMs with NASA’s Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observatory and calculate a corresponding vertical mean bias error of 0.12 m ± 0.75 m at one standard deviation, with an overall RMSE of 0.76 m. We generate the CUDEMs through a standardized process using free and open-source software (FOSS) and provide open-access to our code repository. The CUDEM framework consists of systematic tiled geographic extents, spatial resolutions, and horizontal and vertical datums to facilitate rapid updates of targeted areas with new data collections, especially post-storm and tsunami events. The CUDEM framework also enables the rapid incorporation of high-resolution data collections ingested into local-scale DEMs into NOAA NCEI’s suite of regional and global DEMs. Future research efforts will focus on the generation of additional data products, such as spatially explicit vertical error estimations and morphologic change calculations, to enhance the utility and scientific benefits of the CUDEM Program. Full article

(This article belongs to the Special Issue Remote Sensing in Marine-Coastal Environments)

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25 pages, 4636 KiB

Open AccessArticle

Towards an Integrated Observational System to Investigate Sediment Transport in the Tidal Inlets of the Lagoon of Venice

by Gian Marco Scarpa, Federica Braga, Giorgia Manfè, Giuliano Lorenzetti and Luca Zaggia

Remote Sens. 2022, 14(14), 3371; https://doi.org/10.3390/rs14143371 - 13 Jul 2022

Cited by 5 | Viewed by 2037

Abstract

An observation system integrating satellite images, in situ water parameters and hydrodynamic measurements was implemented in a tidal inlet of the Venice Lagoon (Northern Adriatic Sea, Italy). The experimental infrastructure was developed to autonomously investigate suspended sediment dynamics in the two channels of the Lido inlet in relation to the longshore currents in the littoral zone and the tidal circulation along the lagoon channel network. It provided time series of turbidity at the surface, water flow and acoustic backscatter, which was converted into turbidity along the vertical column during different tidal phases and meteo-marine conditions. Accurate turbidity maps were derived from Sentinel-2 (Copernicus) and Landsat 8 (NASA) satellites. Long-term in situ data from field surveys enabled the calibration and intercalibration of the instrumental setup and validation of satellite-derived products. Time series from the instrumental network were analyzed in order to evaluate the temporal variability of suspended sediment in relation to tidal phases and the different meteo-marine conditions. The integration of available datasets with satellite images also permitted the testing of the methodology for a 3-D reconstruction of the suspended sediment pattern in calm sea conditions, under the effect of the sole hydrodynamical forcing. Remotely sensed data provide a synoptic distribution of turbidity in the inlet area allowing the analysis of the surficial patterns of suspended sediment and the inferring of information on the transport processes at different spatial scales. In calm sea conditions, the results show that the transport is driven by tidal currents with a net seaward transport related to a larger export of materials from the northern basin of the Lagoon of Venice. During typical northeasterly storms, materials mobilized on the beaches and in the shoreface are transported into the inlet and distributed into the lagoon channel network, following the flood tidal currents and determining net import of materials. The multitude of information provided by this system can support research on aquatic science (i.e., numerical simulations) and address end-user community practices. The ecosystem management will also benefit operational purposes, such as the monitoring of morphological transformations, erosion processes and planning of coastal defense in the future scenarios of sea level rise. The developed approach will also help to understand how the regulation of the inlet flow introduced by the operation of the flood barriers will affect the fluxes of particles and, in the long term, the lagoon morphodynamics. Full article

(This article belongs to the Special Issue Remote Sensing in Marine-Coastal Environments)

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20 pages, 32969 KiB

Open AccessArticle

Mapping Tidal Flats of the Bohai and Yellow Seas Using Time Series Sentinel-2 Images and Google Earth Engine

by Maoxiang Chang, Peng Li, Zhenhong Li and Houjie Wang

Remote Sens. 2022, 14(8), 1789; https://doi.org/10.3390/rs14081789 - 07 Apr 2022

Cited by 15 | Viewed by 3700

Abstract

Tidal flats are one of the most productive ecosystems on Earth, providing essential ecological and economical services. Because of the increasing anthropogenic interruption and sea level rise, tidal flats are under great threat. However, updated and large-scale accurate tidal flat maps around the Bohai and Yellow Seas are still relatively rare, hindering the assessment and management of tidal flats. Based on time-series Sentinel-2 imagery and Google Earth Engine (GEE), we proposed a new method for tidal flat mapping with the Normalized Difference Water Index (NDWI) extremum composite around the Bohai and Yellow Seas. Tidal flats were derived from the differences of maximum and minimum water extent composites. Overall, 3477 images acquired from 1 Oct 2020 to 31 Oct 2021 produced a tidal flat map around the Bohai and Yellow Seas with an overall accuracy of 94.55% and total area of 546,360.2 ha. The resultant tidal flat map at 10 m resolution, currently one of the most updated products around the Bohai and Yellow Seas, could facilitate the process of sustainable policy making related to tidal flats and will help reveal the processes and mechanisms of its responses to natural and human disturbance. Full article

(This article belongs to the Special Issue Remote Sensing in Marine-Coastal Environments)

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18 pages, 16639 KiB

Open AccessArticle

Continuous, High-Resolution Mapping of Coastal Seafloor Sediment Distribution

by Sara Innangi, Michele Innangi, Mirko Di Febbraro, Gabriella Di Martino, Marco Sacchi and Renato Tonielli

Remote Sens. 2022, 14(5), 1268; https://doi.org/10.3390/rs14051268 - 05 Mar 2022

Cited by 2 | Viewed by 2257

Abstract

Seafloor topography and grain size distribution are pivotal features in marine and coastal environments, able to influence benthic community structure and ecological processes at many spatial scales. Accordingly, there is a strong interest in multiple research disciplines to obtain seafloor geological and/or habitat maps. The aim of this study was to provide a novel, automatic and simple model to obtain high-resolution seafloor maps, using backscatter and bathymetric multibeam system data. For this purpose, we calibrated a linear regression model relating grain size distribution values, extracted from samples collected in a 16 km² area near Bagnoli–Coroglio (southern Italy), against backscatter and depth-derived covariates. The linear model achieved excellent goodness-of-fit and predictive accuracy, yielding detailed, spatially explicit predictions of grain size. We also showed that a ground-truth sample size as large as 40% of that considered in this study was sufficient to calibrate analogous regression models in different areas. Regardless of some limitations (i.e., inability to predict rocky outcrops and/or seagrass meadows), our modeling approach proved to be a flexible tool whose main advantage is the rendering of a continuous map for sediment size, in lieu of categorical mapping approaches which usually report sharp boundaries or rely on a few sediment classes. Full article

(This article belongs to the Special Issue Remote Sensing in Marine-Coastal Environments)

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